Embodiments described herein relate generally to segmentation of images, such as three-dimensional (3D) medical images.
The method by which objects represented in images are labeled is referred to as segmentation.
Objects may be manually labeled by an expert, and this is considered to be ‘the gold standard’ in view of the sophistication of an expert in recognizing objects, such as a clinician identifying organs and their sub-structure in two-dimensional (2D) or 3D X-ray images.
Automated image processing procedures carried out by computers are widely available to perform segmentation in a host of different applications.
FIG. 1 is a flow chart showing a basic segmentation process.
In Step S1, a novel image data set, referred to in the figure as a patient image data set, is provided to a computer for segmentation.
In Step S2, a registration is carried out to align the novel image data set with a reference image data set which has already been segmented and is stored with its segmentation data. The reference image data set with its segmentation data is referred to as an atlas data set, or simply an atlas. The atlas carries the segmentation data in a set of masks which indicate labeled features, such as one or more anatomical features that have been labeled manually. This alignment step is referred to in the prior art as registration. For example, in the case of a 3D medical image, the atlas has its anatomical regions of interest labeled, so that all voxels belonging to a particular anatomical region are marked with a common label. Typically registration is done with a series of transformations including rigid and non-rigid transformations, and at least one warp transformation.
In Step S3, the registered novel image data set is segmented. Segmentation is the process by which the atlas labels are propagated, or applied, to the novel image data set.
In Step S4, the segmented novel image data set, i.e. the novel image data set and the associated segmentation data, is output. In the case of 3D medical image data, the novel image data set will be loaded into and displayed by a volume rendering program, which will exploit the segmentation data to allow selective configuration of display parameters based on the segmentation data.
Volume rendering is a visualization method for viewing two-dimensional (2D) representations of three-dimensional (3D) data sets and is widely used in medical imaging. Multi-planar reformatting (MPR) is a type of volume rendering often used for processing 3D data sets collected by medical imaging equipment.
In the field of 3D medical imaging, rather than using only one atlas to segment every novel image data set, it has become well known to use multiple atlases in the segmentation process.
Multi-atlas segmentation proceeds by making some kind of intelligent selection or weighting between or among multiple atlases to produce the best results for the particular novel image data set being processed, or at least a better result on average than if only a single atlas was used. Intuitively, the value of multi-atlas segmentation in medical imaging can be appreciated, given that any sample human or animal population will not be a continuum, but rather split into defined groups: for example based on age, environment, pathology or ethnicity.
Anatomical variation in the thoracic cavity is a good example of general differences that exist among patients. Cardio-thoracic ratio (CTR) is a measure of the diameter of the heart with respect to the diameter of the thoracic cavity defined for chest X-ray images. CTR has been found to be a good indicator of heart pathology. It has also been shown, that CTR not only varies based on pathology, but also varies with age, body size and ethnicity.
In multi-atlas segmentation applications, the choice of atlas or atlases to perform segmentation on is very important. The overall goal of an organ atlas is to be as typical as possible to an anatomical category, and this poses a challenge to medical image analysis because of underlying variability in anatomy.
FIG. 2 is a flow chart showing the steps of an example multi-atlas segmentation process based on M atlases, where M is a positive integer of 2 or more, typically 2, 3, 4 or 5.
In Step S1, a novel image data set is provided to a computer for segmentation.
In Step S2M, each of the M atlases is aligned with the novel image data set. As illustrated, this is essentially a parallel process, since each of the registrations is independent of the others.
In Step S3M, values of a common similarity measure is computed for each pair of registered images.
In Step S4, the similarity values are compared and based on these values one of the atlases is chosen to be the one to use for segmenting the novel image data set. There are known variations in this general method, such as not choosing a single atlas, but choosing multiple atlases, and consolidating the results using some kind of weighting or voting scheme to determine the labeling of voxels (or pixels). Perhaps the most common similarity measures used are mutual information (MI), normalized mutual information (NMI) and cross-correlation (CC).
In Step S5, the registered novel image data set is segmented using the selected atlas.
In Step S6, the segmented novel image data set, i.e. the novel image data set and the associated segmentation data, is output.